The present invention generally relates to semiconductor manufacturing processes and particularly a system and method for determining, ranking and filtering alias rules for selecting a best alias rule.
An alias rule in a semiconductor manufacturing process refers to a tool step (i.e., a manufacturing step performed in a semiconductor manufacturing tool or a specific component of the tool such as a chamber) that is related to another tool step included in an original rule. For example, assume that to fabricate a wafer, the wafer should go through photolithography 50 times. Then, an alias rule may be a tool step performing the photolithography on the wafer at the first time. A “related” alias rule may be another tool step performing the photolithography on the wafer at the second time. The original rule refers to a potential faulty tool step found by implementing an empirical learning method. The empirical learning method includes, but is not limited to: collecting a large amount of data (e.g., 1 million samples) and then predicting a future outcome (i.e., success/failure of a process or tool or pass/failure of a wafer) based on an analysis on the data.
A semiconductor manufacturing process includes, but not limited to: slicing crystal to obtain wafers, polishing the wafers, growing oxide (e.g., SiO2) on the wafers, performing photolithography on the wafers, removing the oxide on the wafers, diffusing and implanting ions on the wafers, annealing wafers, etc. The photolithography refers to a technique used to define micro-architectures on the wafers.
To find root causes for poor results (e.g., poor wafer yield rates) in the semiconductor manufacturing process, a traditional approach detects potential faulty tools and manufacturing steps by the empirical methods. However, tools that are initially found to be faulty in the traditional approach may be working properly and/or hide an underlying problem (i.e., the root cause of the poor results).
Therefore, it is highly desirable to detect the root causes by evaluating related causes that are alias rules (i.e., related tool steps) to the original rule (i.e., potentially faulty tool steps that are found by the traditional empirical method).